Distributed temperature sensing (DTS) devices are optoelectronic devices which measure temperature by optical fibers functioning as linear sensors. Temperature values are recorded along the optical sensor cable as a continuous profile (temperature trace). A high accuracy of temperature determination is achieved over long distances. Measurement distances of many kilometers can be achieved. The temperature dependence of the Raman effect can be used for a DTS measurement.
A measurement of a physical quantity over space and time, like for example by a Raman-Optical Time Domain Reflectometer (OTDR) which measures the temperature profile over time along a fiber (Distributed Temperature Sensing, DTS) usually contains noise which reduces the ability to resolve small events, like temperature changes at some time or location, leading (in this example) to a limited temperature resolution or limitation in possible sensor length. Measures can be taken to increase the Raman signal or to reduce the noise contributors, but this can cause a high effort and have limits.
Another method is to filter the data to reduce the noise. Such filters may take a measurement trace D(x) along the position x and transfer it (according to the selected filter) into a trace D′(x), hopefully with the desired improvement.
One simple implementation would be to average each point with some neighbor points (sliding spatial averaging). Such a smoothed trace having less noise, but obviously reducing the ability to resolve sharp temperature changes over position is limiting the spatial resolution. EP 2,772,738 A2, US 2014/0241396 try to reduce the noise by spatial filtering, but preserve the spatial resolution of real events (like temperature hot spots or steps) where identified.
In general, by the nature of noise it is not possible to separate an unknown (random) noise from an unknown (real) signal if no additional data is available, so all filtering has to be based on some kind of assumption or knowledge about the noise and/or the signal. This applies to any kind of filter like Wavelet-based, Kalman filters etc. In the above mentioned example (EP 2,772,738 A2, US 2014/0241396) the assumption is basically that a real event in the signal along space, for example a hot spot, is significantly larger in amplitude than the noise, so that any event of sufficient amplitude is identified and that location on the trace is passed unfiltered, i.e. with full spatial resolution. Obviously this is not working with events that are comparably small (not significant) and therefore would get smoothed out. Another side-effect with such significance-based filters is that for example a growing hot-spot will transit from initially being unidentified and thus flattened by the filter to being identified and thus passed unfiltered (not smoothed out). Such a transition adds a non-real dynamic into the displayed hot-spot, for example by letting the hot-spot suddenly pop up, which can lead to wrong conclusions in some applications.
As noise limits accuracy of distributed sensing, it is generally desired to reduce it. Moreover, noise or distortions in the measurement signals of a distributed sensing device may result in an inaccuracy of the physical quantity to be determined.